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OntoFuzz: An Information Retrieval Model in a Multi-Tenant Cloud Environment using Neuro-Fuzzy and Ontological-based Approach

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Title OntoFuzz: An Information Retrieval Model in a Multi-Tenant Cloud Environment using Neuro-Fuzzy and Ontological-based Approach
 
Creator Jindal, Dimpy
Kaushik, Manju
Behl, Barkha
 
Subject Cloud computing
Information retrieval
Multi-tenancy
Neuro-fuzzy
Ontology
 
Description 856-863
There is a tremendous amount of data present on the web and accessing useful/relevant information from a cluster of
random documents is a tedious and time-consuming task. Traditional information retrieval techniques and information
management systems are not that intelligent to extract relevant information from pre-defined datasets or documents. This
necessitates the researchers to create and enhance a sophisticated information retrieval system. Also, the similarity between
information is equipped with uncertainties due to its computing measures. Keeping these issues in mind, a neuro-fuzzy and
ontological-based model in a multi-tenant cloud environment is proposed in this research study. The model comprises
modules like query expansion, the weighting of terms and queries, and hashing function to ease the retrieval process
followed by validation of the dataset using a neuro-fuzzy network to retrieve relevant information from the cloud service
provider. The simulation results prove the validation of the proposed model in terms of higher accuracy and better retrieval
performance as compared to traditional models (support vector machines and deep neural networks) as well as existing
recent works.
 
Date 2024-08-12T10:20:06Z
2024-08-12T10:20:06Z
2024-08
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/64405
https://doi.org/10.56042/jsir.v83i8.812
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.83(8) [August 2024]